Antenna processing processes the observations originating from several sensors.
FIG. 1 shows a system of antennas composed of an array with several antennas receiving several radio-electric sources with different angles of incidence. The antennas of the array receive the sources with a phase and an amplitude dependent on the angle of incidence of the sources, as well as the position of the antennas. FIG. 2 shows that the angles of incidence of the sources can be parametrized either in one dimension, 1D, with the azimuth θm or in two dimensions, 2D, with the angles of azimuth θm and elevation Δm.
Antenna processing techniques utilize the spatial diversity of the sources: use of the spatial position of the antennas of the array so as to better utilize the differences in incidence and in distance of the sources. Antenna processing breaks up into two major areas of activity:                Goniometry, the objective of which is to determine the incidences θm in 1D or the pair of incidences (θm, Δm) in 2D. For this purpose, goniometry algorithms use the observations arising from the antennas or sensors. FIG. 2 shows that goniometry is performed in one dimension, 1D, when the waves from the transmitters propagate in the same plane and that otherwise it is necessary to apply goniometry in two dimensions, 2D. This plane of the waves is often that of the antenna array where the angle of elevation is zero.        Spatial filtering, illustrated in FIG. 3, the objective of which is to extract either the modulated signals sm(t), or the symbols contained in the signal (Demodulation). This filtering consists in combining the signals received on the sensor array so as to form an optimal reception antenna for one of the sources. Spatial filtering can be blind or cooperative. It is cooperative when there exists a priori knowledge about the signals transmitted (direction of arrival, symbol sequences, etc.) and it is blind in the converse case. Included in this activity are the activities of blind separation of sources, matched filtering on direction of arrival (beamforming) or on replicas, multi-sensor MODEM (demodulation), etc.        
The current techniques of multiple input multiple output or MIMO blind demodulation [11] [12][13][14], have notably the drawback of processing only the case of baseband transmitters with 1 sample per symbol. In these techniques, there exist procedures utilizing solely statistics of order 2 [12]. Other procedures are extensions of the CMA technique [11] which, in particular, in single input multiple output or SIMO, have the drawback of converging less empty than order-2 procedures [5] [9]. The procedure in [13] has notably the drawback of demodulating the transmitters one after another by an iterative technique of successive elimination of the transmitters to be demodulated. This approach exhibits the drawback of not processing the transmitters in an equal manner.
The invention relates to a method of blind demodulation of signals arising from one or more transmitters, the signals consisting of a mixture of symbols where the signals are received on a system comprising several receivers characterized in that it comprises at least one step of separating the transmitters by using the temporal independence of the symbol trains {ak-p,i} indexed by “p” specific to a transmitter and the mutual independence of the transmitters, being the index of a transmitter by “i”.